When to Use an AI Template vs a Static Template

Kyber has written extensively about AI-powered status letters, DOI complaint responses, and hybrid templates built with AI blocks. But there is a foundational concept behind all of those capabilities that we have not explicitly defined.

That concept is the Kyber AI template.

AI templates are what make Kyber fundamentally different from traditional customer communications management systems. To understand why, it helps to start with the type of correspondence they were built to handle.

Claims correspondence falls into two very different buckets.

Some letters are high volume and low complexity. They are required, repetitive, and mostly the same every time.

Others are low volume and high complexity. They are the letters adjusters dread because they require judgment, explanation, and careful wording.

Most legacy CCM platforms treat both buckets the same way. They give you static templates that can populate a handful of fields. Then you spend the rest of the time fighting the system to insert the language the template cannot handle, and maintaining brittle logic across states, coverages, and edge cases.

Kyber’s model is simpler.

Use static templates when correspondence should be automated end to end. Use AI templates when correspondence cannot be prewritten once, because the hardest part is storytelling.

Static templates. Best for high volume, low complexity

Static templates win when the content is stable and the goal is throughput.

Think acknowledgements, routine status updates, and other compliance driven notices where the structure is consistent and the variation is mostly a set of known fields. In these cases, a static template is not a compromise. It is the right tool because it enables straight through automation at scale.

This is what you see in the Aspire case study. Aspire automated 44% of outbound claims correspondence within three months by pushing high volume letters into event driven execution, so adjusters were not initiating, selecting, and sending routine notices.

This is also where Kyber’s auto population work matters. A template only delivers automation if it is actually filled. Most CCMs populate the obvious fields, then leave the operationally important pieces manual, like jurisdictional disclaimers, license numbers, logic dependent clauses, and recipient routing. Teams discover the gap during implementation, then end up building and maintaining rules one by one.

If your letter is meant to be “send with minimal review,” static templates plus strong auto population and orchestration are the path to real automation.

AI templates. Best for complex letters that take an hour plus to draft

AI templates win when the work is not repetitive, and the time cost comes from writing and editing what has happened in an easy to digest format for the end claimant.

Partial denials, denials, and reservations of rights are the clearest examples. The structure can be governed, but the content has to adapt to the actual claim. These letters take time because adjusters are doing the heavy lifting. They are deciding what applies, building the explanation, and producing something that is clear, defensible, and compliant.

A Kyber AI template is still a template. It has a defined structure and approved language. The difference is that the sections that normally force freeform writing are generated into a strong first draft tailored to the claim, so the adjuster is reviewing and refining rather than starting from scratch.

This is not a theoretical benefit. In Kyber’s analysis of real revision behavior across AI generated letters, 55% were sent with zero edits. The median letter required no edits before being sent for review.

That is the core promise of AI templates. They are built for the correspondence where the bottleneck is drafting, and where quality and consistency matter most.

The missing middle. Hybrid templates with AI Blocks

Some teams want a bridge between fully static and fully AI driven.

That is where hybrid templates come in. You keep the deterministic sections static, and use AI blocks only where variability actually lives. This lets you preserve tight control while still removing the parts that cause adjusters to write paragraphs from scratch.

How traditional CCMs top out early

Most CCMs are optimized for templating, not for claims correspondence as a living workflow.

They help you manage libraries and populate basic fields. Then, when you hit complex correspondence, you either multiply templates endlessly or push the hardest parts onto the adjuster. Either way, the last mile is still manual. That is why claims teams end up with massive template libraries that are hard to maintain and hard to keep consistent across jurisdictions.

Kyber is built around the idea that both template types are necessary.

Static templates handle the predictable volume. AI templates handle the high variance, high risk letters where the bottleneck is writing.

Over time, teams usually keep both, but they stop pretending one tool can solve both problems.

What this looks like in practice

When carriers adopt this split, you see two outcomes.

First, routine correspondence becomes real automation. Aspire’s 44% automation result in just 90 days is a good example of what happens when high volume letters are treated as system infrastructure, not a task queue for adjusters.

Second, complex correspondence stops being a template management problem. Branch consolidated its managed template library by 80%, reducing operational overhead tied to maintaining hundreds of variants.

And for the letters that still require judgment, the draft quality improves. Adjusters start from something closer to a senior adjuster’s first pass, not a blank page.

The point is not “AI everywhere.”

The point is using static templates where you want straight through execution, and AI templates where the work is inherently variable and the cost lives in drafting and editing.